Nowcasting lightning during RELAMPAGO

2020 ◽  
Author(s):  
Evan Ruzanski ◽  
Venkatachalam Chandrasekar ◽  
Ivan Arias
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
The United States ◽  
Extreme Weather Events ◽  
Optimization Approach ◽  
Lightning Flash ◽  
Convective Systems ◽  
Physical Mechanisms ◽  
Weather Events ◽  
Future Loss

<p>The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) international field campaign occurred June 1, 2018, to April 30, 2019. This campaign was comprised of more than 150 scientists from 10 organizations. Data was collected to investigate different phases of the life cycle of thunderstorms that occur in Argentina to better understand the physical mechanisms that cause the initiation and growth of organized convective systems in some of the most intense storms on the planet. The main focus of the project was to develop new conceptual models for forecasting extreme weather events that will hopefully lead to reductions in future loss of life and property.</p><p>This presentation shows the performance of a recently developed model for estimating ice mass aloft, a key component in the atmospheric electrification process, and a method for nowcasting lightning activity using C-band weather radar and Global Lightning Dataset (GLD360) data from RELAMPAGO. This nowcasting method uses a grid-based approach to make specific forecasts of lightning in space and time. The method estimates ice mass aloft in the region where electrification occurs using a numerical optimization approach to essentially reframe a simplified bulk microphysical model into a completely data-driven model. Previous results using WSR-88D S-band radar data in the United States showed that using this model significantly improved nowcasts of first-flash lightning occurrence versus the traditional weather radar-based ice mass estimator as well as using lightning flash-rate density directly.</p>

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

Extreme Weather and Climate

2017 ◽  
Author(s):  
Friederike Otto
Keyword(s):  
Extreme Events ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Event ◽  
Weather Forecasts ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Weather Events ◽  
Global Mean

Natural disasters and extreme weather events have been of great societal importance throughout history and often brought everyday life to a catastrophic halt, in a way sometimes comparable to wars and epidemics, only without the lead time. Extreme weather events with large impacts serve as an anchor point of the collective memory of the population in the affected area. Every northern German of the right age remembers the storm surge of 1962 and where they were at the time and has friends or family effected by the event. The “dust bowl” of the 1930s with extensive droughts and heat waves shaped the life of a generation in the United States, and the Sahel droughts in the 1960s and 1970s led to famine and dislocation of population on a massive scale the region arguably never quite recovered from. Hurricane Hyian in 2013 is said to have directly influenced the outcome of the annual Conference of the Parties (COP) United Nation Framework Convention for Climate Change Negotiations in Warsaw, leading to the inclusion of a mechanism to deal with loss and damage from climate-related disasters. Though earthquakes are still fairly unpredictable on short timescales, this is not the case for weather events. Weather forecasts today are so good that we normally know the time and location of the landfall of a hurricane within a 100-mile radius days in advance. Improvements in the prediction of slow-onset events such as droughts (which depend on the rainfall over a large region and whole season) are less striking but have still improved dramatically in the late 20th and early 21st centuries. One of the major reasons for the large increase in the accuracy of weather forecasts is the exponential increase in computing power, which allows scientists to predict and study extreme weather events using complex computer models, simulating possible weather events under certain conditions to understand the statistics of and physical mechanisms behind extreme events. Extreme events are by definition rare and thus impossible to understand from historical records of weather observation alone. Despite the progress on our understanding of and ability to predict extreme weather events, substantial uncertainties remain. Two aspects are of particular importance. Firstly, we know that the climate is changing, having observed almost a one-degree increase in global mean temperature. However, global mean temperature doesn’t kill anyone, extreme weather events do. Their frequency and intensity is changing and will continue to change, but the extent of these changes depends on a host of both global and local factors. Secondly, whether or not a rare weather event leads to extreme impacts depends largely on the vulnerability and exposure of the affected societies. If these are high, even a perfectly forecasted weather event leads to disaster.

scholarly journals Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

2018 ◽  
Vol 10 (12) ◽  
pp. 2029 ◽  
Author(s):  
Thomas Ramsauer ◽  
Thomas Weiß ◽  
Philip Marzahn
Keyword(s):  
Spatial Variability ◽  
Winter Season ◽  
Weather Radar ◽  
Radar Data ◽  
Probability Of Detection ◽  
Statistical Measures ◽  
Weather Events ◽  
Precipitation Measurement ◽  
Spatio Temporal ◽  
Global Precipitation

Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

Invasive Saltcedar and Drought Impact Ant Communities and Isopods in South-Central Nebraska

2020 ◽  
Vol 49 (3) ◽  
pp. 607-614 ◽  
Author(s):  
W Wyatt Hoback ◽  
Jessica Jurzenski ◽  
Kerri M Farnsworth-Hoback ◽  
Karl A Roeder
Keyword(s):  
Native Species ◽  
The United States ◽  
Extreme Weather Events ◽  
Ant Communities ◽  
South Central ◽  
Weather Events ◽  
Activity Density ◽  
Tamarix Spp ◽  
Negative Impacts ◽  
Animal Communities

Abstract The establishment and spread of non-native species often results in negative impacts on biodiversity and ecosystem function. Several species of saltcedar, Tamarix spp. L., have been recently naturalized in large portions of the United States where they have altered plant and animal communities. To test the prediction that saltcedar negatively affects invertebrates, we measured ant genera diversity and the activity density of the exotic isopod Armadillidium vulgare Latrielle (Isopoda: Oniscoidea) for 2 yr using pitfall traps located within 30 5-m2 plots with or without saltcedar at a south-central Nebraska reservoir. From 2005 to 2006, we collected 10,837 ants representing 17 genera and 4,953 A. vulgare. Per plot, the average number of ant genera was not different between saltcedar (x̅ = 3.9) and non-saltcedar areas ( x̅ = 3.9); however, saltcedar plots were compositionally different and more similar from plot to plot (i.e., they had lower beta diversity than control plots) in 2005, but not in 2006. Isopods were likewise temporally affected with higher activity density (+89%) in control plots in 2005, but higher activity density (+27%) in saltcedar plots in 2006. The observed temporal differences occurred as the drought that initially enabled the saltcedar invasion became less severe in 2006. Combined, our results suggest that invertebrate groups like ants, which are generally omnivorous, may be better equipped than more specialized taxa like detritivores to withstand habitat changes due to invasions by non-native species, especially during extreme weather events such as prolonged droughts.

Quality Control of Weather Radar Data Using Polarimetric Variables

2014 ◽  
Vol 31 (6) ◽  
pp. 1234-1249 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Christopher Karstens ◽  
John Krause ◽  
Lin Tang
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Weather Radar ◽  
Radar Data ◽  
The United States ◽  
Skill Score ◽  
Ground Clutter ◽  
Local Variance ◽  
Average Probability ◽  
Differential Reflectivity

Abstract Because weather radar data are commonly employed in automated weather applications, it is necessary to censor nonmeteorological contaminants, such as bioscatter, instrument artifacts, and ground clutter, from the data. With the operational deployment of a widespread polarimetric S-band radar network in the United States, it has become possible to fully utilize polarimetric data in the quality control (QC) process. At each range gate, a pattern vector consisting of the values of the polarimetric and Doppler moments, and local variance of some of these features, as well as 3D virtual volume features, is computed. Patterns that cannot be preclassified based on correlation coefficient ρHV, differential reflectivity Zdr, and reflectivity are presented to a neural network that was trained on historical data. The neural network and preclassifier produce a pixelwise probability of precipitation at that range gate. The range gates are then clustered into contiguous regions of reflectivity, with bimodal clustering carried out close to the radar and clustering based purely on spatial connectivity farther away from the radar. The pixelwise probabilities are averaged within each cluster, and the cluster is either retained or censored depending on whether this average probability is greater than or less than 0.5. The QC algorithm was evaluated on a set of independent cases and found to perform well, with a Heidke skill score (HSS) of about 0.8. A simple gate-by-gate classifier, consisting of three simple rules, is also introduced in this paper and can be used if the full QC method is not able to be applied. The simple classifier has an HSS of about 0.6 on the independent dataset.

scholarly journals Income-based Inequality in Post-disaster Migration is Lower in High Resilience Areas: Evidence from U.S. Internal Migration

2021 ◽  
Author(s):  
Ted Hsuan Yun Chen ◽  
Boyoon Lee
Keyword(s):  
Climate Change ◽  
Internal Migration ◽  
Local Level ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Disaster Resilience ◽  
Migration Patterns ◽  
Weather Events ◽  
Post Disaster

Residential relocation following extreme weather events is among the costliest individual-level measures of climate change adaptation. Consequently, they are fraught with inequalities, with disadvantaged groups most adversely impacted. As climate change continues to exacerbate extreme weather events, it is imperative that we better understand how existing socioeconomic inequalities affect climate migration and how they may be offset. In this study we use network regression models to look at how internal migration patterns in the United States vary by disaster-related property damage, household income, and local-level disaster resilience. Our results show that post-disaster migration patterns vary considerably by the income level of sending and receiving counties, which suggests that income-based inequality impacts both access to relocation for individuals and the ability to rebuild for disaster-afflicted areas. We further find evidence that these inequalities are attenuated in areas with higher disaster resilience. However, because existing resilience incentivizes in situ incremental adaptation which can be a long term drain on individual wellbeing and climate adaptation resources, they should be balanced with policies that encourage relocation where appropriate.

Extreme weather events will hit insurance industry

2015 ◽  
Keyword(s):  
Climate Change ◽  
United States ◽  
Insurance Industry ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Causal Connection ◽  
Economic Implications ◽  
Content Type ◽  
Weather Events

Subject The political and economic implications of greater scientific understanding of extreme weather events. Significance Preparatory talks for the UN climate summit in Paris have seen representatives from developing countries ask the United States and EU for greater compensation for damages caused by extreme weather. The link between climate change and more extreme weather events is clear -- energy from higher temperature levels can be translated into kinetic energy and disrupts usual weather patterns -- but distinguishing the extent of a causal connection, especially for specific events, has until recently been difficult. Impacts Extreme weather events will affect the insurance industry, agriculture, tourism, and food and beverage sectors. In the United States, the South-east will see the highest risks of coastal property losses due to climate change impacts. Hurricanes and other coastal storms combined with rising sea levels are likely to cause growing annual storm losses in the Caribbean. Infrastructure will grow in cost as it must be proofed against new extremes in weather stress.

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